Herding Effect based Attention for Personalized Time-Sync Video Recommendation

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201

Analyzing Nico Nico Douga Videos by Using Users’ Interests and the Distribution of Comments

Nico Nico Douga (NND) is one of the most famous social media platforms for sharing videos in Japan. There are 18 million videos posted to NND, for which users search by using keyword search and related video recommendation. However, it is difficult for the users to find interesting videos because videos generally are associated with only short texts and a few tags. In this paper, we present a method for analyzing videos in NND by using the distribution of time-synchronized comments. Our method regards a video as the set of the users’ comments on it and enables the clustering of videos based on the users’ shared interests. In our experiment, we applied the proposed method to videos posted to NND, and evaluated our method by quantitatively comparing it with existing text-based methods and by qualitatively performing the subjective evaluation of clustering results. In the result of the quantitative evaluation, the proposed method showed a higher score of normalized mutual information than the existing methods when category metadata were used as correct results. The experimental results of the qualitative evaluation showed that the proposed method was as good as or better than the existing text-based and image-based methods